Some enterprises maintain infrastructures made up of large numbers of infrastructure components for delivering some product or service. For example, an electrical power utility may maintain a power grid made up of infrastructure components such as generators, control systems, safety systems, switching equipment, circuit breakers, transmission lines including transmission towers, insulators, power poles, transformers, circuit breakers, and the like.
All components of an enterprise infrastructure in general will degrade over time. The rate at which these components degrade will vary depending upon the nature of the component as well as the use to which the component is put and the environment in which the component is used. Failure of components may have various consequences. In some cases, the consequences may be minor. In other cases, the consequences may be very serious. For example, failure of certain components may result in long term power outages, damage to the environment, and/or serious injury or even death.
In general, an enterprise such as an electrical power utility will want to maintain at least some components of its infrastructure on an proactive basis so as to reduce the likelihood that those components will fail. Maintaining a large infrastructure can be exceedingly expensive. Therefore, the enterprise in general will wish to optimize the use of its resources by carefully planning which components of its infrastructure to replace or upgrade and when to do such replacements or upgrades. Resources may be wasted in replacing some components too early, while the components still have a very low failure risk. On the other hand, resources may be wasted if a component is allowed to fail and the consequences of that failure are expensive or otherwise unacceptable.
For a given type of component, such as a utility pole, it is generally possible to predict the likelihood of failure of the component as a function of time. Such predictions may, for example, be based on studies of when other components of a similar type have failed in a large population of components, or on surveys of the condition of the components. Using such information, an enterprise may optimize its program of replacement and upgrading of infrastructure components by, for each component, calculating a likelihood of failure in a given planning window, determining the likely consequence of such a failure, and prioritizing components to replace or upgrade based on such results. The computational cost of such optimization tends to increase very rapidly with the number of variables for which the optimization is conducted. The number of variables generally scales with the number of infrastructure components.
Enterprises generally use computer systems to facilitate such prioritization, particularly in large enterprises with complicated infrastructure made up of potentially many hundreds of thousands of components. However, the computation involved in such prioritization may be extremely computationally intensive due to the large number of components and computing steps involved. This may require that enterprises allocate significant time and/or resources to such prioritization, since powerful computer systems and/or lengthy computation times may be required in order to perform the necessary computations. This may place a substantial burden on enterprises, particularly where there is a desire to compare multiple scenarios based on different prioritizations. Accordingly, there is a need for more computationally-efficient methods and apparatus for prioritizing components for replacement and/or upgrading. There is also a need for more efficient methods and apparatus for performing other calculations which involve determining the risk that individual components in a large infrastructure may fail.